Temperature Prediction Intelligent System Based on BP Neural Network in Wireless Industrial IoT

With the development of industrial Internet of Things, manual production in the factory has gradually changed to automated production, and the temperature requirements in many aspects of the production process are very strict. In order to control temperature more intelligently, reduce errors and improve production efficiency, through the study of BP neural network, this paper designs and implements an intelligent temperature prediction system based on BP neural network for wireless industrial Internet of Things. Through the selection of intelligent algorithms, the establishment of the model and the realization of the system, a temperature prediction system suitable for the actual production environment of the industrial Internet of Things and improving the accuracy according to the intelligent algorithm is finally realized. The system is relatively complete in function. Temperature prediction and error correction can accurately predict the change of temperature. It can meet the requirements of industrial production control and make the temperature control more stable.

I. Introduction

In the industrial production process, in order to monitor and diagnose the production, sensors are used to collect a large amount of data, such as reactor temperature, indoor temperature, circulating water temperature and other information, various temperatures directly affect the quality and progress of production, so temperature control and forecasting is a very important part. Therefore, temperature prediction technology has been widely used in automated production processes.